Difference between revisions of "Emerging Technologies"

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Revision as of 07:19, 3 January 2018

Research, development, and adoption of innovative, but not widely adopted, tools and technologies for leveraging and enhancing the value of healthcare and clinical data. This can include use of existing or immature technologies in innovative ways.


Working Group Overview

New challenges in regulatory science and drug, biologic, and device development provide new opportunities for recognizing and leveraging new or emerging technologies and computational tools or underutilized existing technologies. Initiated at the 2013 PhUSE Annual CSS, the Emerging Technologies working group provides a forum for determining interest in specific computational science topics, tools, technologies, and approaches.

This Emerging Technologies working group will be an open, transparent forum for sharing pre-competitive means of applying new technologies and is being challenged with creation of well-defined collaborative projects that will describe, prioritize, assess, and assist advancement of these opportunities. Possible topics include (but are not limited to) semantic web applications, analysis metadata, modeling, simulation, and “The Cloud”. Projects incorporating these topics might include prioritization, development, and piloting for feasibility and value.

Leadership Team

Name Role Organization E-mail
Geoff Low ET Co-Lead Medidata Solutions geoff.low (at) phuse.eu
Ian Fleming ET Co-Lead Medidata Solutions Ian.Fleming (at) phuse.eu
Lilliam Rosario Steering Committee Liaison FDA Lilliam.Rosario (at) fda.hhs.gov
Crystal Allard Group Coordinator FDA Crystal.Allard (at) fda.hhs.gov
Steve Wilson FDA Co-Lead FDA Stephen.Wilson (at) fda.hhs.gov

PhUSE CSS 2017

The following projects are meeting at 2017 CSS

The Agenda is as follows:

Current Projects

Cloud Adoption in the Life Sciences Industry

Data Visualisations for Clinical Data

Investigating the use of FHIR in Clinical Research

Clinical Trials Data as RDF

Introduction to Clinical Development Design (CDD) Framework

BlockChain Technology

Past Projects

Metadata Management


Statistical Computing Environments

Proposed Projects

After the Emerging Technology Round Table Sessions the following projects were identified as prospective working groups for the 2015 CSS and beyond. Some of these need passionate people to lead!

Framework for mHealth

As personal fitness trackers become more ubiquitous, industry is starting to assess how these devices can be used to supplement the clinical record. Very much in its infancy, use of these personal devices is a grey area in terms of adoption status, information quality (for example, the use of proprietary data gathering algorithms), patient privacy and informed consent. This group will develop a framework so companies looking to adopt these devices in their clinical trials can make an informed decision as they assess the potential of integrating mobile health with clinical research.

  • Sponsor: Tony Hewer
  • Needs a Lead?: Maybe

Machine Learning for Clinical Research

With the continually raising profile of Data Science in the Pharmaceutical Industry, techniques which have traditionally not been widely adopted in the industry are starting to become part of a clinical analyst's toolbox. Text Analytics, already employed in other industries, enables analysts to uncover clinically important information in previously opaque free-text. Techniques to be discussed include sentiment analysis, concept/entity extraction, clustering and other approaches. This group will gather people interested in text mining techniques to develop frameworks and approaches for extracting structured information from unstructured clinical trial text data.

  • Sponsor: CDER
  • Needs a Lead?: Yes

Big Data approaches in Clinical Research

Big Data is a term we are all familiar with - it is promoted as the way to comprehensively answer multiple questions across multiple domains. But what is Big Data? Is it relevant to clinical researcher? Is it simply large volumes of data? Is it these large datasets plus the techniques needed to extract value from this data? What approaches work and when? This group will begin by answering the most basic questions associated with Big Data and clinical research -- is it relevant, and where do we go from here? This topic has been previously posed and we have a Project Request: File:ET-project-Template-BigData.doc

  • Sponsor:
  • Needs a Lead?: Yes

Webinar Presentations


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